Show simple item record

dc.contributor.authorHe, Mingzhu
dc.contributor.authorKimball, John S.
dc.contributor.authorManeta, Marco P.
dc.contributor.authorMaxwell, Bruce D.
dc.contributor.authorMoreno, Alvaro
dc.contributor.authorBegueria, Santiago
dc.contributor.authorWu, Xiaocui
dc.date.accessioned2018-12-04T17:00:36Z
dc.date.available2018-12-04T17:00:36Z
dc.date.issued2018-03
dc.identifier.citationHe, Mingzhu, John S. Kimball, Marco P. Maneta, Bruce D. Maxwell, Alvaro Moreno, Santiago Begueria, and Xiaocui Wu. "Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data." Remote Sensing 10, no. 3 (March 2018). DOI:10.3390/rs10030372.en_US
dc.identifier.issn2072-4292
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/15036
dc.description.abstractAccurate crop yield assessments using satellite remote sensing-based methods are of interest for regional monitoring and the design of policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations is generally too coarse to capture cropland heterogeneity. The fusion of data from different sensors can provide enhanced information and overcome many of the limitations of individual sensors. In thitables study, we estimate annual crop yields for seven important crop types across Montana in the continental USA from 2008-2015, including alfalfa, barley, maize, peas, durum wheat, spring wheat and winter wheat. We used a satellite data-driven light use efficiency (LUE) model to estimate gross primary productivity (GPP) over croplands at 30-m spatial resolution and eight-day time steps using a fused NDVI dataset constructed by blending Landsat (5 or 7) and Terra MODIS reflectance data. The fused 30-m NDVI record showed good consistency with the original Landsat and MODIS data, but provides better spatiotemporal delineations of cropland vegetation growth. Crop yields were estimated at 30-m resolution as the product of estimated GPP accumulated over the growing season and a crop-specific harvest index (HIGPP). The resulting GPP estimates capture characteristic cropland productivity patterns and seasonal variations, while the estimated annual crop production results correspond favorably with reported county-level crop production data (r = 0.96, relative RMSE = 37.0%, p < 0.05) from the U.S. Department of Agriculture (USDA). The performance of estimated crop yields at a finer (field) scale was generally lower, but still meaningful (r = 0.42, relative RMSE = 50.8%, p < 0.05). Our methods and results are suitable for operational applications of crop yield monitoring at regional scales, suggesting the potential of using global satellite observations to improve agricultural management, policy decisions and regional/global food security.en_US
dc.description.sponsorshipUSDA (658 2016-67026-25067, 365063); NASA (80NSSC18M0025, NNX14AI50G, NNX14A169G, NNX08AG87A); Montana Research and Economic Development Initiativeen_US
dc.language.isoenen_US
dc.rightsCC BY, This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/legalcodeen_US
dc.titleRegional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Dataen_US
dc.typeArticleen_US
mus.citation.issue3en_US
mus.citation.journaltitleRemote Sensingen_US
mus.citation.volume10en_US
mus.identifier.categoryEngineering & Computer Scienceen_US
mus.identifier.doi10.3390/rs10030372en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentLand Resources & Environmental Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.data.thumbpage11en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

CC BY, This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.
Except where otherwise noted, this item's license is described as CC BY, This license lets you distribute, remix, tweak, and build upon this work, even commercially, as long as you credit the original creator for this work. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials.

MSU uses DSpace software, copyright © 2002-2017  Duraspace. For library collections that are not accessible, we are committed to providing reasonable accommodations and timely access to users with disabilities. For assistance, please submit an accessibility request for library material.